The field of superintelligence is undergoing rapid development, with global R&D investments growing steadily. Breakthroughs in quantum machine learning, neuromorphic computing, and brain-computer interfaces are advancing artificial general intelligence (AGI) from concept to reality. This transformation is reshaping diverse sectors including healthcare, energy, and aerospace at an unprecedented pace. Journal of Superintelligence and Computing is established against this backdrop, supported by Wuchang University of Technology and published by CoSci Press, aiming to foster academic exchange and bridge technological innovation with societal needs.

We envision a future where superintelligence serves as humanity's most powerful collaborator—enhancing healthcare through intelligent diagnostics, optimizing environmental protection via climate modeling, and accelerating scientific breakthroughs with advanced algorithms. This journal aspires to catalyze not only computational advancements but also the boundless potential of human civilization.

JSC focuses on but is not limited to the following hot topics:

• Novel computing architectures: High-performance computing systems and brain-inspired chips
• Intelligent algorithm innovation: New machine learning methods and cognitive models
• Practical applications: Implementation of intelligent technologies in key domains

Journal of Superintelligence and Computing adheres to the principles of openness, innovation, and rigor, committed to becoming an internationally influential academic journal in the field of intelligent convergence, and contributing to the transformation and development of computing technology.

We welcome innovative, practical research that advances technology while addressing real-world challenges. The journal encourages exploration of sustainable AI development pathways to better serve human society. The scope of JSC includes but is not limited to the following areas:

• Superintelligence foundations: Cognitive science, machine learning, deep learning, neural networks
• High-performance computing: Distributed architectures, heterogeneous systems, quantum computing, energy-efficient technologies
• Machine learning methods: Explainable AI, few-shot/meta-learning, multimodal learning, robust/adversarial learning
• Ethical frameworks: Algorithmic fairness, privacy preservation, ethical decision models, policy/regulation
• Neuromorphic engineering: Spiking neural networks, neuromorphic chips, bio-inspired learning, synaptic plasticity
• Interdisciplinary applications: AI in healthcare, education, smart cities, environmental protection
• Societal impacts: Human-AI collaboration, educational transformation, cultural influence